Context-Aware Group Recommendation for Point-of-Interests

Zhu, Qiliang; Wang, Shangguang; Cheng, Bo; Sun, Qibo; Yang, Fangchun; Chang, Rcc

doi:10.1109/access.2018.2805701

Cited by 33 publications

(13 citation statements)

References 39 publications

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“…This method is a more extensive research method, which first generates the evaluation of individual users on the project, then integrates the evaluation of all users, and finally gives a unified recommendation result. Zhu aggregated individual recommendations for group recommendation [19]. Furthermore, they proposed a context-aware POI group recommendation by taking into account rationality of location and intra-group influences.…”

Section: Aggregate Recommendationmentioning

confidence: 99%

A Collaborative Filtering Algorithm with Intragroup Divergence for POI Group Recommendation

2021

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The purpose of POI group recommendation is to generate a recommendation list of locations for a group of users. Most of the current studies first conduct personal recommendation and then use recommendation strategies to integrate individual recommendation results. Few studies consider the divergence of groups. To improve the precision of recommendations, we propose a POI group recommendation method based on collaborative filtering with intragroup divergence in this paper. Firstly, user preference vector is constructed based on the preference of the user on time and category. Furthermore, a computation method similar to TF-IDF is presented to compute the degree of preference of the user to the category. Secondly, we establish a group feature preference model, and the similarity of the group and other users’ feature preference is obtained based on the check-ins. Thirdly, the intragroup divergence of POIs is measured according to the POI preference of group members and their friends. Finally, the preference rating of the group for each location is calculated based on a collaborative filtering method and intragroup divergence computation, and the top-ranked score of locations are the recommendation results for the group. Experiments have been conducted on two LBSN datasets, and the experimental results on precision and recall show that the performance of the proposed method is superior to other methods.

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Section: Aggregate Recommendationmentioning

confidence: 99%

A Collaborative Filtering Algorithm with Intragroup Divergence for POI Group Recommendation

2021

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“…Most group recommendation strategies are based on the aggregation of recommendations, preferences or proles [10]. Adaptations of the recommendation aggregation functions are proposed in [16,18,1]. In [16] the authors present a group POI recommendation method (GA-GPOI) that combines users' gregariousness, i.e.…”

Section: Related Workmentioning

confidence: 99%

“…the degree of association between users in a community, activity, and sign-in times. Zhu et al [18] consider the rationality of the location, based on distance and the intra-group inuence when making group decisions about POIs. The group decision strategy aims to reach a consensus on the POIs based on a function that aggregates the recommendation results taking into account group relevance and group disagreement.…”

Section: Related Workmentioning

confidence: 99%

A MAS-Based Approach for POI Group Recommendation in LBSN

Schiaffino

Godoy

Pace

et al. 2020

Advances in Practical Applications of Agents, Multi-Agent Systems, and Trustworthiness. The PAAMS Collection

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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“…Tewari et al [10] proposed a book recommendation system based on combine features of content based filtering, collaborative filtering, and association rule mining. Zhu et al [11] take into account the importance of location and the intragroup influence in POI group recommendation and employ distance prefiltering and distance ranking adjustment to improve recommendation satisfaction.…”

Section: Recommendationmentioning

confidence: 99%

Personalized POI Recommendation Based on Subway Network Features and Users’ Historical Behaviors

Yan

Zhao

Guo

2018

Wireless Communications and Mobile Computing

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Current recommender systems often take fusion factors into consideration to realize personalize point-of-interest (POI) recommendation. Historical behavior records and location factors are two kinds of significant features in most of recommendation scenarios. However, existing approaches usually use the Euclidean distance directly without considering the traffic factors. Moreover, the timing characteristics of users’ historical behaviors are not fully utilized. In this paper, we took the restaurant recommendation as an example and proposed a personalized POI recommender system integrating the user profile, restaurant characteristics, users’ historical behavior features, and subway network features. Specifically, the subway network features such as the number of passing stations, waiting time, and transfer times are extracted and a recurrent neural network model is employed to model user behaviors. Experiments were conducted on a real-world dataset and results show that the proposed method significantly outperforms the baselines on two metrics.

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Context-Aware Group Recommendation for Point-of-Interests

Cited by 33 publications

References 39 publications

A Collaborative Filtering Algorithm with Intragroup Divergence for POI Group Recommendation

A Collaborative Filtering Algorithm with Intragroup Divergence for POI Group Recommendation

A MAS-Based Approach for POI Group Recommendation in LBSN

Personalized POI Recommendation Based on Subway Network Features and Users’ Historical Behaviors

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